The growing proliferation of wearable computing devices has brought on the age of affective computing. There is growing number of applications that utilize the wide array of sensors that are now available, in order to leverage measurements of users' affective response.
Measurements of affective response typically involve values representing physiological signals and/or behavioral cues. There are many algorithms that are capable of translating these values into emotional responses. Thus, it is possible to know how a user feels about an experience the user has based on various signals such as heart rate, respiration, galvanic skin response, and even facial images.
Since measurements of affective response can represent, often quite accurately, how users feel, these measurements may comprise in them the effects of various cognitive processes, and in particular they may reflect a wide array of biases the users have. A bias may be considered a tendency, an attitude, an inclination, and/or a world view a person may have. It is natural for a person's reaction to reflect that person's biases, therefore, it is natural to find bias in measurements of affective response. However, there are scenarios in which having bias in measurements may be problematic.
In some scenarios, measurements of affective response may be used for crowd-based applications, such as computing scores for experiences based on measurements of affective response of users. If, for example, measurements containing bias are used to compute a score for a certain experience, then in some cases, the score may reflect, at least to a certain extent, the individual tendencies and inclinations of the users of whom the measurements were taken; this may produce a skewed value for the score, rather than serve as having it be an objective metric of the quality of the experience.
Since a person's measurements reflect the person's biases, enabling other entities to acquire a person's measurements of affective response may enable the entity to learn about a person's biases. In some cases, even providing measurements that are used to compute crowd-based results, such as scores for experiences based on measurements of multiple users, may also be cause for concern. Aggregating such data and employing various “big data” analysis techniques may reveal information about users, such as some of their biases, which they might prefer to keep private.
Thus, as the use of affective computing grows, there is a need to address issues such as bias in measurements of affective response and privacy concerns, in order to improve the user experience and maintain user privacy.